.png){kind=icon}
返回
Temporal network embedding framework with causal anonymous walks representations
.png){kind=icon}
OA
AI
DOI：10.7717/peerj-cs.858.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Many tasks in graph machine learning, such as link prediction and node classification, are typically solved using representation learning. Each node or edge in the network is encoded via an embedding. Though there exists a lot of network embeddings for static graphs, the task becomes much more complicated when the dynamic (i.e., temporal) network is analyzed. In this paper, we propose a novel approach for dynamic network representation learning based on Temporal Graph Network by using a highly custom message generating function by extracting Causal Anonymous Walks. We provide a benchmark pipeline for the evaluation of temporal network embeddings. This work provides the first comprehensive comparison framework for temporal network representation learning for graph machine learning problems involving node classification and link prediction in every available setting. The proposed model outperforms state-of-the-art baseline models. The work also justifies their difference based on evaluation in various transductive/inductive edge/node classification tasks. In addition, we show the applicability and superior performance of our model in the real-world downstream graph machine learning task provided by one of the top European banks, involving credit scoring based on transaction data.
Keyword:
Temporal networks
Dynamic networks
Temporal network embedding
Temporal random walks
Temporal graph attention
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
2.5
论文数:
3.4K
被引数:
6.9K
机构
引用论文
Towards multi-modal causability with Graph Neural Networks enabling information fusion for explainable AI使用图形神经网络实现多模式因果关系，从而实现可解释AI的信息融合.png)
INFORMATION FUSIONOAAI
INFORMATION FUSIONIF15.5
OAAI分享收藏Survey on graph embeddings and their applications to machine learning problems on graphs图嵌入及其在图上机器学习问题中的应用综述OAAI
OAAI分享收藏
查看更多内容